2020 ESA Annual Meeting (August 3 - 6)

SYMP 20 Abstract - How open data science enables better science in less time: Lessons from the Ocean Health Index

Tuesday, August 4, 2020: 4:10 PM
Julia Lowndes1,2 and Ben Halpern1, (1)National Center for Ecological Analysis and Synthesis, University of California Santa Barbara, Santa Barbara, CA, (2)National Center for Ecological Analysis and Synthesis, Santa Barbara, CA
Background/Question/Methods

The Ocean Health Index (OHI) is a scientific goal-oriented framework and tool for incorporating the best available scientific information into marine policy. First published in 2012, OHI global assessments have been repeated annually and have been used in nearly 20 governmental-management-academic collaborations around the world. It has been an indicator for the United Nations Convention on Biological Diversity (and considered for Sustainable Development Goal 14) and included in United States policy as part of the first Ocean Plan in the Northeast. But as a team of marine scientists, we had never been taught how to work responsibly with data or code so we could work reproducibly and collaboratively. So we found out the hard way that our default approaches were not reproducible by even ourselves. Getting through this involved quite a reckoning, but when we got through it, we knew we had a story to tell.

Results/Conclusions

Some keys to these Ocean Health Index successes thus far are its inclusiveness – bringing people, teams, data, and knowledge together – and its flexibility and transparency as an assessment tool. And, it has been because we embraced data science and open practices and dramatically improved how we do science. Community-taught with R, RStudio/RMarkdown, and GitHub, our workflow is more reproducible, collaborative, open, and with more emphasis on communication, documentation, training, and mentorship. Sharing our path to better science in less time (Lowndes et al. 2017, Nature Ecology and Evolution) has empowered others in the scientific community to do the same – so that we can all uncover data-driven solutions faster.